Landslide Susceptibility Modelling of Central Highland Part of Chaliyar River Basin, Kerala, India with Integrated Algorithms of Frequency Ratio and Shannon Entropy

Suraj P.R., Melvin Babu, Manoharan A.N., Archana Krishnan N., Shruthi Mayya K., Niveditha P.
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Abstract

An integrated landslide susceptibility analysis is carried out for the central highland region of the Chaliyar River Basin in Kerala, India using bivariate statistical methods, namely the Frequency Ratio (FR) and Shannon Entropy (SE). The study addresses the complex nature of landslides, influenced by natural as well as anthropogenic factors, with specific focus on assessing the landslide likelihood of the study area. The methodology involves a systematic approach of collecting the inventory data, identifying various landslide causative factors and developing their corresponding thematic maps, spatial analysis of landslide occurrence and causative factors using GIS software and generation of Landslide Susceptibility Model (LSM) employing FR and SE algorithm, followed by model validation. Various causative factors considered for the study include slope angle, slope aspect, slope curvature, elevation, lithology, drainage density, landuse and landcover (LULC), Topographic Wetness Index (TWI) and Normalized Difference Vegetation Index (NDVI). The FR and SE algorithm enable the spatial classification of the study area into four landslide susceptibility categories namely Low, Moderate, High, and Very High. Validation of both the LSMs was carried out using Landslide Density Index (LDI) and Area Under the Curve (AUC) methods. LDI demonstrate a positive fit for both the models, which is indicative of reliability of the susceptibility predictions of the study area. A slightly higher AUC value of SE model is an indication of a high accuracy rate of SE model over FR model. This research brings out a robust methodology for predicting and identifying the landslide risks of the study area.The outcomes of this study will help in developing effective strategies to manage the landslide hazards in geologically vulnerable areas. Keywords: Landslide Susceptibility, Landslide Conditioning Factors, Frequency Ratio, Shannon Entropy, Landslide Density Index(LDI), Area Under the Curve (AUC)
利用频率比和香农熵综合算法建立印度喀拉拉邦 Chaliyar 河流域中部高地部分的滑坡易发性模型
采用双变量统计方法,即频率比(FR)和香农熵(SE),对印度喀拉拉邦 Chaliyar 河流域的中部高地地区进行了综合滑坡易发性分析。该研究探讨了受自然和人为因素影响的山体滑坡的复杂性,特别侧重于评估研究区域的山体滑坡可能性。研究方法包括采用系统方法收集清单数据、确定各种滑坡致灾因素并绘制相应的专题地图、使用地理信息系统软件对滑坡发生情况和致灾因素进行空间分析、采用熵算法和 SE 算法生成滑坡易发性模型(LSM),然后对模型进行验证。研究中考虑的各种成因包括坡角、坡面、坡曲、海拔、岩性、排水密度、土地利用和土地覆盖(LULC)、地形湿度指数(TWI)和归一化植被指数(NDVI)。利用 FR 和 SE 算法,可将研究区域划分为四个滑坡易发类别,即低度、中度、高度和极高度。使用滑坡密度指数(LDI)和曲线下面积(AUC)方法对两种 LSM 进行了验证。山体滑坡密度指数(LDI)显示两个模型的拟合度均为正,这表明研究区域的易感性预测是可靠的。SE 模型的 AUC 值略高于 FR 模型,表明 SE 模型的准确率较高。这项研究为预测和识别研究区域的滑坡风险提供了一种可靠的方法。这项研究的成果将有助于制定有效的战略,以管理地质脆弱地区的滑坡灾害。关键词滑坡易感性、滑坡条件因子、频率比、香农熵、滑坡密度指数(LDI)、曲线下面积(AUC)
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